None

Clindamycin

Vancomycin

Ciprofloxacin

Ampicillin

Cefaperazone

Metronidazole

Streptomycin

diversity vs colonization

alpha diversity

communities colonized to higher levels have lower diversity (alpha)

association between cfu and alpha (invsimpson and shannon - NS)

cfu vs # of otus (NS)

shared otus?

beta diversity

highly infected communities are most different than untreated

separation between untreated mice and all the highly infected communities (>1e6)

communities that recover/elimnate cdifficile are more diverse

difference in diversity between highly infected

more change w/low diversity?

more change with high cfu?

need to remove dependence of daily sampling?

Alpha Diversity

## [[1]]

## [[1]]
## 
## Call:
## lm(formula = get(variable_name) ~ CFU, data = data_frame)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.162 -2.412 -1.213  1.429 13.075 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  5.241e+00  1.714e-01   30.58  < 2e-16 ***
## CFU         -1.455e-08  2.479e-09   -5.87 8.24e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.272 on 468 degrees of freedom
##   (36 observations deleted due to missingness)
## Multiple R-squared:  0.06859,    Adjusted R-squared:  0.0666 
## F-statistic: 34.46 on 1 and 468 DF,  p-value: 8.244e-09
## [[1]]

## [[1]]
## 
## Call:
## lm(formula = get(variable_name) ~ CFU, data = data_frame)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -35.753 -15.274  -4.381  12.851  72.405 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  4.609e+01  1.047e+00   44.03  < 2e-16 ***
## CFU         -1.104e-07  1.514e-08   -7.29 1.33e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 19.99 on 468 degrees of freedom
##   (36 observations deleted due to missingness)
## Multiple R-squared:  0.102,  Adjusted R-squared:  0.1001 
## F-statistic: 53.14 on 1 and 468 DF,  p-value: 1.331e-12
## [[1]]

## [[1]]
## 
## Call:
## lm(formula = get(variable_name) ~ CFU, data = data_frame)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.65813 -0.51173 -0.06297  0.45801  2.09371 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.899e+00  3.404e-02  55.801  < 2e-16 ***
## CFU         -3.098e-09  4.923e-10  -6.293 7.15e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6498 on 468 degrees of freedom
##   (36 observations deleted due to missingness)
## Multiple R-squared:  0.07803,    Adjusted R-squared:  0.07606 
## F-statistic: 39.61 on 1 and 468 DF,  p-value: 7.149e-10
## [[1]]

## [[1]]
## 
## Call:
## lm(formula = get(variable_name) ~ CFU, data = data_frame)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.9720 -1.5949 -0.8509  0.8313 14.2621 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  4.051e+00  1.503e-01  26.944  < 2e-16 ***
## CFU         -6.395e-09  1.924e-09  -3.324 0.000978 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.435 on 366 degrees of freedom
##   (36 observations deleted due to missingness)
## Multiple R-squared:  0.0293, Adjusted R-squared:  0.02665 
## F-statistic: 11.05 on 1 and 366 DF,  p-value: 0.0009777
## [[1]]

## [[1]]
## 
## Call:
## lm(formula = get(variable_name) ~ CFU, data = data_frame)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -28.215 -12.009  -2.285   9.603  58.318 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.882e+01  9.625e-01  40.329  < 2e-16 ***
## CFU         -6.051e-08  1.232e-08  -4.913 1.36e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 15.59 on 366 degrees of freedom
##   (36 observations deleted due to missingness)
## Multiple R-squared:  0.06187,    Adjusted R-squared:  0.0593 
## F-statistic: 24.14 on 1 and 366 DF,  p-value: 1.356e-06
## [[1]]

## [[1]]
## 
## Call:
## lm(formula = get(variable_name) ~ CFU, data = data_frame)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.43246 -0.41705 -0.08106  0.43371  1.47089 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.674e+00  3.495e-02  47.885  < 2e-16 ***
## CFU         -1.551e-09  4.473e-10  -3.468 0.000588 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5662 on 366 degrees of freedom
##   (36 observations deleted due to missingness)
## Multiple R-squared:  0.03181,    Adjusted R-squared:  0.02916 
## F-statistic: 12.02 on 1 and 366 DF,  p-value: 0.0005875

After talking with Pat (1/17/18) Does end point look like initial? are most similar the recovered ones? Split analysis by abx/dose/days recovered Resistance more similar than colonized? Start with high dose and 1 day recovery then look at how modulating the dose/recovery affects train model with low recovery and test with high recovery Show different context of day 0 compare differences in metro recovery how do susceptibility break points compare?

the metadata file has the following columns group CFU - ranges 0 to 8.1e8 with 601 NAs (most of NAs are on days when cdiff was not present, so can change to 0 expect for NAs after day 1) cage mouse day - ranges -11 to 10 abx - amp cef cipro clinda metro none strep vanc 405 379 83 190 339 3 362 312 dose - 0.1 0.3 0.5 0.625 1 10mg/kg 5 NA’s 304 253 653 112 339 273 136 3 dose abx cages mice none 1 1 0.5 amp
10 cipro
10 clinda
0.1 cef
0.3 cef 0.5 cef 1 metro 0.1 strep 0.5 strep 5 strep 0.1 vanc 0.3 vanc 0.625 vanc cdiff - if sample was treated challenged with C. difficile logical T(1770), F(303) delayed - if sample was allowed extra days to recover from abx treatment logical T(455), F(1618) preAbx - if sample collected prior to abx treatment logical T(154), F(1919) recovDays - how many days after stopping abx (metro and amp for 5 day recovery) range 1 to 5

only one mouse not given abx but is listed as preAbx F for -5 possible to denote mock abx treatment(?) question about mouse 600-2D-6 (cef - delivered via water) should be pre-antibiotic but is listed as F all other mice in cage are preAbx on day -6, except this one since this abx was delivered via drinking water, it is likely clerical error, need to write a check script to make sure all mice in each cage all are recorded to have the same treatment

## # A tibble: 100 x 6
##    otu       median_abundance    rho   pvalue pvalue_BH pvalue_bon
##    <chr>                <dbl>  <dbl>    <dbl>     <dbl>      <dbl>
##  1 Otu000003            1.26  -0.614 4.17e-50  7.76e-48   7.76e-48
##  2 Otu000064            0.    -0.606 1.52e-48  1.41e-46   2.83e-46
##  3 Otu000070            0.    -0.531 1.40e-35  6.88e-34   2.61e-33
##  4 Otu000006            0.    -0.531 1.48e-35  6.88e-34   2.75e-33
##  5 Otu000041            0.    -0.484 5.64e-29  2.10e-27   1.05e-26
##  6 Otu000010            1.65   0.482 1.05e-28  3.26e-27   1.96e-26
##  7 Otu000017            0.100 -0.470 3.47e-27  8.35e-26   6.46e-25
##  8 Otu000057            0.    -0.470 3.59e-27  8.35e-26   6.68e-25
##  9 Otu000031            0.    -0.459 6.39e-26  1.32e-24   1.19e-23
## 10 Otu000050            0.    -0.453 3.83e-25  7.11e-24   7.11e-23
## # ... with 90 more rows